CNN-Script

import pandas as pd
import numpy as np
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.layers import Dense, Conv2D, MaxPool2D, AvgPool2D,Flatten, Input, Activation, Dropout, BatchNormalization
from keras.models import Sequential
from keras import Model
from keras import initializers
from keras import optimizers
import matplotlib.pyplot as plt

train = pd.read_csv("data/train.csv")
test = pd.read_csv("data/test.csv")

y_train = train['label'].values

train = train.ix[: ,train.columns != 'label'].values
test = test.values

train = train/255
test = test/255

train = train.reshape(-1,28,28,1)
test = test.reshape(-1,28,28,1)


target = pd.get_dummies(y_train).values
print(target.shape)

model = Sequential()
model.add(Conv2D(input_shape=(28,28,1),filters=10,kernel_size=(5,5),padding='same', kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.05, seed=17)))
model.add(MaxPool2D(pool_size=(3,3), strides=(2,2)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(filters=16,kernel_size=(3,3),padding='same', kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.05, seed=17)))
model.add(MaxPool2D(pool_size=(3,3), strides=(1,1)))
model.add(Activation('relu'))
model.add(Flatten(input_shape=(26,26,6)))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer=optimizers.nadam(lr=0.00005), metrics=['categorical_accuracy'])

print(model.summary())



history = model.fit(x=train, y=target,batch_size=128, epochs=200, validation_split=0.1, verbose=2, shuffle=True)

y_pred = model.predict(test, batch_size=128)

np.save("mnist_pred.csv", y_pred)

print(history.history.keys())
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.plot(loss)
plt.plot(val_loss)
plt.legend(['loss', 'val_loss'])
plt.show()